Technology's next big frontier is teaching machines to learn from their own mistakes

m.a.r.c. / flickrTechnology is all about progress: We build tools to make life easier and learn how to improve those tools over time.

But in the past couple of decades, technology has improved dramatically thanks to the internet and the proliferation of internet-enabled devices, which have allowed us to create and build upon ideas at an incredible clip.

Now, the next big frontier is to teach our technologies to learn and improve on their own.

This concept is called "machine learning," and it's at the heart of most artificial intelligence systems you see.

Self-driving technology is one notable example of machine learning. The goal there is for cars to be autonomous: We want them to drive, navigate, and react like a human can — even better, in fact — to reduce the number of fatal accidents (over 32,000 in 2013, according to the Insurance Institute for Highway Safety), and improve the overall efficiency of ground transportation.

We want to get from A to B quicker, and more safely, too. But in approaching this problem, auto and tech companies like Google and Audi don't try to teach their cars to know every possible situation that might occur.

Instead, they take the cars on the road and continuously gather data, logging every single mile. This data covers everything from the state of the car to the state of the environment to how a driver reacts in any given situation.

YouTube/AudiHere's why machine learning is so important here: Something needs to be done with all that data. Companies like Tesla have built large intelligence networks to absorb and crunch this information, which is basically used to inform every other car in the "fleet" — any vehicle touched by the network, basically.

But machine learning won't just be the key to driverless cars; this technology is also going to help people get the most out of their computers, including their smartphones and tablets.

"Machine learning is a core, transformative way by which we're re-thinking about how we're doing everything," Pichai said. "We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we're in early days, but you will see us — in a systematic way — apply machine learning in all these areas."

The next Apple TV will also utilize machine learning to predict what you want to watch, thanks to the addition of Siri. apple This ability to absorb and analyze massive amounts of information, simply for the sake of improving a product's performance, is going to go a long way for tech companies like Google and Apple. Their smart assistants — Google Now and Siri, respectively — have become much more proactive this year through some big software updates. Instead of you repeatedly asking for information, these digital assistants are now trying to learn your habits and anticipate what you might need— an app, someone's contact information, sports scores — before you ask.

Since the basis of machine learning is statistics, and the ability to perform better through experiences, this kind of technology is also driving important advances in medicine, genetics, robotics, the internet, advertising, even video games. This list serves as a pretty good example for why a multifaceted technology company like Google is investing so heavily in machine learning.

Keep in mind, however, machine learning is not the same thing as artificial intelligence, though you might see the two working together in plenty of future consumer-leaning applications. Machine learning is more about creating algorithms that can generalize data to regularly improve performance and behaviors. Artificial intelligence covers areas beyond machine learning, particularly natural language processing and understanding.

But the jury's still out on artificial intelligence: despite its importance, there is plenty of reluctance and a legitimate sense of fear around this issue. You know, since a sentient robot or machine that can think independently improve its own software could easily perceive humans as a threat (to itself, to the environment, etc.), and decide to wipe them/us out. Most people, however, seem to be all for machine learning, as it will help people get the most out of the tools we have, promoting even more progress across all technologies and disciplines, so we can live happier, healthier, easier lives.